Bubbles in Crude Oil and Commodity Energy Index: New Evidence

Floros, Christos; Galyfianakis, Georgios

doi:10.3390/en13246648

Cited by 18 publications

(11 citation statements)

References 41 publications

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“…The literature on bubble formation in commodities and asset markets is often built on Phillips et al's (2013Phillips et al's ( , 2015 theoretical works. Several studies, in addition to identifying multiple bubble episodes, have tried to identify the events leading to the formation of the commodity and asset price bubbles (see Balcilar et al, 2014;Caspi et al, 2014;Floros & Galyfianakis, 2020;Gronwald, 2016;Khan, Su, & Rehman, 2021;Li et al, 2022;Liu & Lee, 2018;Su et al, 2017Su et al, , 2020Umar et al, 2021;Yang et al, 2021). Some of the studies in this area have found that economic policy uncertainty has an asymmetric effect on energy consumption, meaning that adverse changes in uncertainty have a larger impact than positive changes (Sarker et al, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

“…This study joins the literature that employs the SADF and GSADF tests to identify the specific periods of price exuberance. These studies include Su et al (2017), Liu and Lee (2018), Floros and Galyfianakis (2020), Su et al (2020), Umar et al (2021), Khan, Su, Umar, et al (2021), Li et al (2022), andYang et al (2021). In addition, the use of the logit model to determine the impact of uncertainties on price exuberance can be seen as an extension of the work of Wang and Kim (2022).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Global uncertainties, geopolitical risks and price exuberance: Evidence from international energy market

Mamman,

Iliyasu,

Ahmed

et al. 2024

OPEC Energy Review

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The interaction of global uncertainty and geopolitical risks with energy price fluctuations has remained a critical global issue. This interaction can impact several regions' macroeconomic performance and welfare by making fundamental energy price forecasting more difficult, which may lead to exuberant behaviour. To help producers, consumers, and regulators make informed decisions in the face of volatile and uncertain energy markets, it is critical to highlight how these uncertainties influence price exuberance. In this light, this study examines the impact of global uncertainty and geopolitical risks on international energy price exuberance using monthly data from January 1990 to October 2022. The study employs supremum augmented Dickey–Fuller (SADF) and generalised augmented Dickey–Fuller (GSADF) tests to identify energy price exuberance. Firstly, consistent with exuberant behaviour, the tests identify seven episodes of explosive behaviour in the international energy prices within the sample. Secondly, this study applies the Logit model to estimate the impact of global uncertainty and geopolitical risks on price exuberance. The estimates suggest that the heightening of global uncertainty may deflate the price exuberance. This study also observes that adverse geopolitical risks (threats and acts) in the world and Ukraine amplify the likelihood of price exuberance in the market. However, adverse geopolitical risk (GPR) in Russia negatively impacted the formation of price exuberance. This finding implies that policymakers can use global uncertainty and geopolitical risks as early warning indicators of probable price exuberance in the international energy market. The findings also indicate the need for a buffer system and safe passage for the flow of energy supply in a geopolitical conflict or a major global event. The study further shows the need for a coordinated effort in innovation, research, and development to enhance energy efficiency and minimise reliance on fossil fuels, which these uncertainties may not significantly influence.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Global uncertainties, geopolitical risks and price exuberance: Evidence from international energy market

Mamman,

Iliyasu,

Ahmed

et al. 2024

OPEC Energy Review

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“…Numerous studies have been conducted to investigate price bubbles in energy markets. Floros and Galyfianakis (2020) employed the Supremum Augmented Dickey-Fuller (SADF) test to detect bubbles in BRENT and WTI crude oil prices, with the former index being the benchmark for the latter. Alternately, Su et al (2017) found multiple explosive bubbles in the WTI crude oil market from 1985 to 2016 using the GSADF test.…”

Section: Price Bubbles In Energy Marketsmentioning

confidence: 99%

Untitled

2023

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The important role played by energy markets has concerned investors regarding the market's behaviour and interconnections when seeking asset diversification strategies, which has become critical in financial analysis. This study aimed to identify price bubbles in energy futures markets and asset co-movements with the crude palm oil futures market (FCPO). This study utilised five futures indices from January 2, 2001, to October 30, 2020. Three methods were employed to explain the behaviour of the energy futures markets: The Generalized Supremum Augmented Dickey-Fuller (GSADF) test, the wavelet power spectrum technique and the wavelet coherence method. The findings from the GSADF test revealed that all energy future markets indicated the presence of asset bubbles, which were influenced by geopolitical events and speculation. The study also demonstrated the presence of periods of high volatility across multiple horizons, which occasionally occurred around the same period as explosive price behaviour. The results of the wavelet coherence method showed that the FCPO market had high co-movement with the Brent crude oil (BRENT) and heating oil (HOIL) markets and, to a lesser extent, with the natural gas (NGAS) and light sweet crude oil (WTI) markets. By linking the GSADF and wavelet approaches, the present study showed how energy price bubbles, their volatility, and co-movements with the FCPO market were related in the presence of geopolitical events and speculation. The present study's findings have suggested strategies regulators and investors can use to manage investment risk and portfolio diversification.

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“…Other papers in the field (e.g., Maslyuk and Smyth [6]) have focused on various non-renewables energy series, with their analyses relying on a wide variety of techniques. Unit-root/stationarity testing is also being employed to detect potential bubbles in asset prices (e.g., Perifanis [7] and Floros and Galyfianakis [8] have studied the case of crude oil prices).…”

Section: Introductionmentioning

confidence: 99%

Stationarity in the Prices of Energy Commodities. A Nonparametric Approach

2021

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In this paper, we address the classical problem of testing for stationarity in the prices of energy-related commodities. A panel of fourteen time series of monthly prices is analyzed for the 1980–2020 period. Nine of the series are classical nonrenewable, GHG-emissions-intensive resources (coal, crude oil, natural gas), whereas the remaining, low-emission group includes both uranium and four commodities employed in biofuels (rapeseed, palm, and soybean oils, and ethanol). A nonparametric, bootstrap-based stationarity testing framework is employed. The main advantage of this procedure is its asymptotically model-free nature, being less sensitive than parametric tests to the risks of misspecification and detection of spurious unit roots, although it has the potential limitation of typically requiring larger samples than mainstream tools. Results suggest that most of the series analyzed may be trend stationary. The only exception would be crude oil, where different conclusions are obtained depending on whether a seasonal correction is applied or not.

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Bubbles in Crude Oil and Commodity Energy Index: New Evidence

Cited by 18 publications

References 41 publications

Global uncertainties, geopolitical risks and price exuberance: Evidence from international energy market

Global uncertainties, geopolitical risks and price exuberance: Evidence from international energy market

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Stationarity in the Prices of Energy Commodities. A Nonparametric Approach

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